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Unlocking Efficiency in Gas Lift System Operation: A Data Driven Approach for Early Problem Identification Using Machine Learning and Digitalized Two-Pen Recorder Chart

Year: 2024 Authors: Mustaqim, S., D., Yasutra, A. Reference: IPA24-E-314
Keywords: Machine Learning, Random Forest, accuracy, continuous gas lift, gas lift wells,
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Proceedings Title : Proc. Indon. Petrol. Assoc., 48th Ann. Conv., 2024

Conventional monitoring and identification of problems in continuous gas lift wells are time-consuming, involving data collection and analysis activit...

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Revolutionizing Failure Detection with Machine Learning

Year: 2025 Authors: Komala, D., R., Lazuardi, H., Tanjungsari, F. Reference: IPA25-E-394
Keywords: Downtime reduction, Electric Submersible Pumps (ESP), Failure Prediction, Historical Data, Machine Learning, Predictive Analytics, Random Forest, Reliability, Sensor Data, oil production,
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Proceedings Title : Proc. Indon. Petrol. Assoc., 49th Ann. Conv., 2025

A failed Electrical Submersible Pump (ESP) well is typically identified when there is suboptimal flow or no flow reaching the surface. The process of...

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Data-Driven Early Detection Model for Slugging in Offshore Gas Production Using Machine Learning: Case Study of NB Field

Year: 2025 Authors: Edyawan, W., Setiawan, D. Reference: IPA25-E-400
Keywords: Early detection, Machine Learning, Offshore gas production, Operational management, Predictive modeling, Random Forest, Reserve management, Slugging, data-driven approach, production optimization,
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Proceedings Title : Proc. Indon. Petrol. Assoc., 49th Ann. Conv., 2025

Slugging presents significant operational challenges in offshore gas production, mainly when fluids from multiple wells are transported from an offsho...

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